Research on Granular Concept Cognitive Learning System Under MapReduce Framework

MI Yun-long; LI Jin-hai; LIU Wen-qi; LIN Jing

doi:10.3969/j.issn.0372-2112.2018.02.005

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Research on Granular Concept Cognitive Learning System Under MapReduce Framework

更新时间：2025-07-16

- Research on Granular Concept Cognitive Learning System Under MapReduce Framework
- Acta Electronica Sinica Vol. 46, Issue 2, Pages: 289-297(2018)
- 作者机构：
  
  1. 昆明理工大学理学院,云南,昆明,650500
  2. 怀化学院计算机科学与工程学院,湖南,怀化,418000
  3. 武陵山片区生态农业智能控制技术湖南省重点实验室,湖南,怀化,418000
  4. 昆明理工大学理学院,云南,昆明,650500
  5. 怀化学院计算机科学与工程学院,湖南,怀化,418000
  6. 武陵山片区生态农业智能控制技术湖南省重点实验室,湖南,怀化,418000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61305057, No.61562050, No.61573173)
- DOI：10.3969/j.issn.0372-2112.2018.02.005
  CLC： TP18
- Published Online：25 February 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
MI Yun-long, LI Jin-hai, LIU Wen-qi, et al. Research on Granular Concept Cognitive Learning System Under MapReduce Framework[J]. Acta Electronica Sinica, 2018, 46(2): 289-297.
DOI：

MI Yun-long, LI Jin-hai, LIU Wen-qi, et al. Research on Granular Concept Cognitive Learning System Under MapReduce Framework[J]. Acta Electronica Sinica, 2018, 46(2): 289-297. DOI： 10.3969/j.issn.0372-2112.2018.02.005.

摘要

针对经典的概念学习算法难以处理大规模数据集的问题，本文提出一种基于MapReduce框架的粒概念认知学习并行算法.该算法借鉴认知心理学的知觉和注意认知思想，并融合粒计算的粒转移原理.首先构建适应大数据环境的粒概念并行求解算法，并与经典粒概念构造算法做了对比，在此基础上分别从外延和内涵角度建立了粒概念认知计算系统，然后对给定对象集或属性集进行认知概念学习.实验结果表明，该并行算法是有效的，适合海量数据的粒概念认知学习.

Abstract

Considering that the classical concept learning algorithms are difficult to deal with the massive data set

a MapReduce-based parallel algorithm for granular concept cognitive learning is proposed. The parallel algorithm is based on the cognitive thoughts of perception and attention in cognitive psychology

and it is combined with the granule transformation principle of granular computing. Specifically

a parallel algorithm is developed to compute granular concepts in big data environment

and a comparative analysis of the parallel algorithm and the classical granular concept construction algorithm is made. Granular concept cognitive computing systems are also constructed from the perspectives of extension and intension. Then

cognitive concept learning is performed by a given object set or attribute set. Experimental results show that the proposed parallel algorithm is effective and can be suitable for granular concept cognitive learning of massive data.

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Multi-Level Description of Granules From an Outsider’s Perspective

Horizontal Union Algorithm of Multiple Concept Lattices

Survey of MapReduce Parallel Programming Model

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